Lead Data Architect (Fabric)

We are looking for an experienced Lead Data Architect – Microsoft Fabric to design, build, and operationalise scalable enterprise data platforms on Microsoft Azure. This is a senior, hands-on architecture role requiring strong expertise in data engineering, cloud data platforms, DevOps, and Microsoft Fabric.

You will work closely with data engineers, analysts, enterprise architects, product owners, and business stakeholders to deliver modern data solutions that support analytics, artificial intelligence, machine learning, and business intelligence workloads.

Important: This is a senior-level position. Candidates must have at least 8 years of overall professional experience, with 10+ years preferred, and relevant hands-on experience with Microsoft Fabric or equivalent Azure data platform technologies.

Key Responsibilities

1. Enterprise Architecture and Technical Leadership

• Lead the end-to-end architecture, design, and implementation of enterprise Microsoft Fabric solutions.
• Define technical architecture, engineering standards, reusable frameworks, and best practices for platform adoption.
• Own the architecture of the unified data ecosystem, including OneLake structure, workspace strategy, capacity planning, and domain segregation.
• Establish medallion architecture patterns across Bronze, Silver, and Gold data layers.
• Provide technical leadership to data engineers and developers, ensuring quality, scalability, and consistency.
• Review solution designs, code, and deployment strategies to ensure alignment with architectural standards.

2. Microsoft Fabric Platform Architecture

• Design and implement enterprise data platforms using Microsoft Fabric components, including OneLake, Lakehouse, Data Warehouse, Data Factory, Data Engineering, Real-Time Intelligence, and Power BI.
• Architect scalable, cloud-native data solutions supporting analytics, AI, and business intelligence requirements.
• Demonstrate strong understanding of OneLake, Lakehouse and Warehouse architecture, Delta Lake, Parquet formats, and medallion architecture.
• Define workspace and capacity strategies, domain-level segregation, and platform design standards.
• Leverage Fabric capabilities to establish a unified, scalable, and secure enterprise data environment.

3. Data Engineering and Integration

• Design and develop robust ETL/ELT pipelines using Microsoft Fabric Data Factory and Spark Notebooks.
• Integrate structured, semi-structured, and unstructured data from cloud-based and on-premises systems.
• Implement data ingestion, transformation, cleansing, validation, and orchestration processes.
• Optimise ingestion strategies to ensure reliability, scalability, and performance.
• Work with SQL, Python, PySpark, and Apache Spark to build and optimise data engineering solutions.

4. Data Modelling and Analytics

• Design dimensional data models, Lakehouse architectures, and enterprise data warehouses.
• Develop semantic models and datasets to support Power BI reporting and self-service analytics.
• Translate business and analytical requirements into scalable data architecture and engineering solutions.
• Establish data modelling standards and reusable patterns across enterprise workloads.

5. DevOps, CI/CD and Automation

• Define and implement DevOps practices for Microsoft Fabric, including Git integration, deployment pipelines, and environment promotion across Development, QA, UAT, and Production.
• Build and maintain CI/CD pipelines using Azure DevOps or GitHub Actions.
• Automate deployment of Fabric artefacts, including Lakehouses, Warehouses, Notebooks, Pipelines, and Semantic Models.
• Implement automated testing, validation, release management, and rollback strategies.
• Establish infrastructure-as-code and source control practices for Fabric workloads.

6. Security, Governance and Compliance

• Architect and enforce enterprise security and access controls across Microsoft Fabric.
• Implement workspace roles, OneLake data access policies, sensitivity labels, and row-level security.
• Work with Microsoft Entra ID, Role-Based Access Control (RBAC), and Microsoft Purview to establish governance frameworks.
• Ensure compliance with enterprise security, privacy, and regulatory requirements.
• Establish consistent data governance and access management practices.

7. Performance Optimisation

• Optimise Spark workloads, SQL Warehouse performance, and OneLake storage utilisation.
• Implement partitioning, indexing, caching, and workload optimisation strategies.
• Monitor platform performance, identify bottlenecks, and proactively improve system efficiency.
• Ensure scalability, reliability, and efficient resource utilisation across data workloads.

8. Cloud Migration and Platform Modernisation

• Lead migration initiatives from legacy data platforms, including Azure Synapse Analytics, Azure Data Factory, on-premises SSIS/SQL Server, and other cloud data warehouses, to Microsoft Fabric.
• Define migration strategies, architecture patterns, and implementation roadmaps.
• Ensure smooth integration, data consistency, and minimal disruption during platform transitions.

9. AI and Advanced Analytics

• Enable AI, Machine Learning, and Copilot capabilities within Microsoft Fabric.
• Support data science workloads using Spark, notebooks, and machine learning models.
• Collaborate with AI teams to operationalise predictive analytics and intelligent data solutions.
• Leverage AI capabilities to improve engineering productivity and data platform efficiency.

10. Production Support and Stakeholder Management

• Lead production deployments and post-deployment support activities.
• Monitor platform health, troubleshoot incidents, and ensure SLA compliance.
• Establish disaster recovery, backup, and business continuity strategies.
• Collaborate with enterprise architects, product owners, business stakeholders, and cross-functional teams.
• Participate in architecture review boards and technical governance meetings.
• Communicate technical solutions, implementation plans, and project status to senior leadership.

11. Team Leadership and Mentoring

• Mentor data engineers and developers on Microsoft Fabric architecture and engineering best practices.
• Conduct code reviews, design reviews, and technical knowledge-sharing sessions.
• Drive continuous improvement initiatives and promote engineering excellence.

Required Qualifications and Skills

• Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related discipline.
• 10+ years of experience in Data Engineering, Data Warehousing, or Cloud Data Platform development.
• Minimum 8+ years of overall professional experience is mandatory.
• 4+ years of hands-on experience with Microsoft Azure Data Platform technologies.
• At least 2 years of hands-on experience implementing Microsoft Fabric in enterprise environments.
• Strong expertise in SQL, Python, PySpark, and Apache Spark optimisation.
• Hands-on experience with Microsoft Fabric components, including OneLake, Lakehouse, Data Warehouse, Data Factory, Power BI, and Real-Time Intelligence.
• Experience with Azure Data Lake Storage (ADLS), Azure DevOps, GitHub, and CI/CD pipelines.
• Strong understanding of dimensional data modelling, ETL/ELT architecture, and data governance.
• Proven experience designing scalable data platforms and leading enterprise-level data engineering initiatives.

Must-Have Skills

• Microsoft Fabric
• Databricks
• PySpark

Good-to-Have Skills

• SQL
• Azure Data Factory
• ETL/ELT

Preferred Qualifications

• Microsoft certifications such as DP-600 (Fabric Analytics Engineer), DP-203 (Azure Data Engineer), or Azure Solutions Architect Expert.
• Experience with Power BI semantic model optimisation and DirectLake mode.
• Familiarity with data mesh and domain-driven data architecture principles.
• Experience with Databricks, Azure Synapse Analytics, Azure AI Services, or Microsoft Purview.
• Knowledge of Agile/Scrum delivery methodologies.
• Strong communication, stakeholder management, problem-solving, and technical leadership skills.